====== Support Vector Machines ======
===== Papers =====
  * Optimizing SVMs in the primal:
    * [[http://is.tuebingen.mpg.de/fileadmin/user_upload/files/publications/primal_[0].pdf|Chapelle 2006 - Training a Support Vector Machine in the Primal]]
    * [[https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.73.2438&rep=rep1&type=pdf|Ratliff et al 2007 - (Online) Subgradient Methods for Structured Prediction]]
  * The idea for training neural networks with SVM loss came from [[https://ronan.collobert.com/pub/matos/2004_phdthesis_lip6.pdf|Ronan Collobert's 2004 PhD Thesis]] and this paper: [[https://ronan.collobert.com/pub/matos/2004_links_icml.pdf|Collobert 2004]] which has some conceptual errors (mainly that SVM loss is not Perceptron loss).

===== Structured SVM (SSVM) =====
See [[https://en.wikipedia.org/wiki/Structured_support_vector_machine|Wikipedia - Structured SVM]].

=== Structured SVM with Latent Variables ===
  * [[https://www.cs.cornell.edu/people/tj/publications/yu_joachims_09a.pdf|Learning Structural SVMs with Latent Variables]] Latent Structured SVM